[Misc] Upgrade to pytorch 2.5

CMakeLists.txt

@@ -49,7 +49,7 @@ set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx940;gfx941;gfx942;gfx1030;gfx11
 # requirements.txt files and should be kept consistent.  The ROCm torch
 # versions are derived from Dockerfile.rocm
 #
-set(TORCH_SUPPORTED_VERSION_CUDA "2.4.0")
+set(TORCH_SUPPORTED_VERSION_CUDA "2.5.0")
 set(TORCH_SUPPORTED_VERSION_ROCM "2.5.0")
 
 #
@@ -507,7 +507,7 @@ else()
   FetchContent_Declare(
           vllm-flash-attn
           GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG 013f0c4fc47e6574060879d9734c1df8c5c273bd
+          GIT_TAG 5259c586c403a4e4d8bf69973c159b40cc346fb9
           GIT_PROGRESS TRUE
           # Don't share the vllm-flash-attn build between build types
           BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn

cmake/utils.cmake

@@ -424,11 +424,7 @@ function (define_gpu_extension_target GPU_MOD_NAME)
   # Don't use `TORCH_LIBRARIES` for CUDA since it pulls in a bunch of
   # dependencies that are not necessary and may not be installed.
   if (GPU_LANGUAGE STREQUAL "CUDA")
-    if ("${CUDA_CUDA_LIB}" STREQUAL "")
-      set(CUDA_CUDA_LIB "${CUDA_CUDA_LIBRARY}")
-    endif()
-    target_link_libraries(${GPU_MOD_NAME} PRIVATE ${CUDA_CUDA_LIB}
-      ${CUDA_LIBRARIES})
+    target_link_libraries(${GPU_MOD_NAME} PRIVATE CUDA::cudart CUDA::cuda_driver)
   else()
     target_link_libraries(${GPU_MOD_NAME} PRIVATE ${TORCH_LIBRARIES})
   endif()

pyproject.toml

@@ -6,7 +6,7 @@ requires = [
     "packaging",
     "setuptools>=61",
     "setuptools-scm>=8.0",
-    "torch == 2.4.0",
+    "torch == 2.5.0",
     "wheel",
     "jinja2",
 ]

requirements-build.txt

@@ -4,6 +4,6 @@ ninja
 packaging
 setuptools>=61
 setuptools-scm>=8
-torch==2.4.0
+torch==2.5.0
 wheel
 jinja2

requirements-cuda.txt

@@ -4,7 +4,7 @@
 # Dependencies for NVIDIA GPUs
 ray >= 2.9
 nvidia-ml-py # for pynvml package
-torch == 2.4.0
+torch == 2.5.0
 # These must be updated alongside torch
-torchvision == 0.19   # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
-xformers == 0.0.27.post2; platform_system == 'Linux' and platform_machine == 'x86_64'  # Requires PyTorch 2.4.0
+torchvision == 0.20   # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
+xformers == 0.0.28.post2; platform_system == 'Linux' and platform_machine == 'x86_64'  # Requires PyTorch 2.5.0

requirements-openvino.txt

@@ -1,7 +1,7 @@
 # Common dependencies
 -r requirements-common.txt
 
-torch == 2.4.0 #  should be aligned with "common" vLLM torch version
+torch == 2.5.0 #  should be aligned with "common" vLLM torch version
 openvino >= 2024.4.0 # since 2024.4.0 both CPU and GPU support Paged Attention
 
 optimum @ git+https://github.com/huggingface/optimum.git@main # latest optimum is used to support latest transformers version

tests/models/decoder_only/language/test_big_models.py

@@ -8,7 +8,7 @@
 
 from vllm.platforms import current_platform
 
-from ...utils import check_outputs_equal
+from ...utils import check_logprobs_close, check_outputs_equal
 
 MODELS = [
     "meta-llama/Llama-2-7b-hf",
@@ -43,18 +43,40 @@ def test_models(
     dtype: str,
     max_tokens: int,
 ) -> None:
-    with hf_runner(model, dtype=dtype) as hf_model:
-        hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
 
-    with vllm_runner(model, dtype=dtype, enforce_eager=True) as vllm_model:
-        vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
-
-    check_outputs_equal(
-        outputs_0_lst=hf_outputs,
-        outputs_1_lst=vllm_outputs,
-        name_0="hf",
-        name_1="vllm",
-    )
+    if model == "openbmb/MiniCPM3-4B":
+        # the output becomes slightly different when upgrading to
+        # pytorch 2.5 . Changing to logprobs checks instead of exact
+        # output checks.
+        NUM_LOG_PROBS = 8
+        with hf_runner(model, dtype=dtype) as hf_model:
+            hf_outputs = hf_model.generate_greedy_logprobs_limit(
+                example_prompts, max_tokens, NUM_LOG_PROBS)
+
+        with vllm_runner(model, dtype=dtype, enforce_eager=True) as vllm_model:
+            vllm_outputs = vllm_model.generate_greedy_logprobs(
+                example_prompts, max_tokens, NUM_LOG_PROBS)
+
+        check_logprobs_close(
+            outputs_0_lst=hf_outputs,
+            outputs_1_lst=vllm_outputs,
+            name_0="hf",
+            name_1="vllm",
+        )
+    else:
+        with hf_runner(model, dtype=dtype) as hf_model:
+            hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
+
+        with vllm_runner(model, dtype=dtype, enforce_eager=True) as vllm_model:
+            vllm_outputs = vllm_model.generate_greedy(example_prompts,
+                                                      max_tokens)
+
+        check_outputs_equal(
+            outputs_0_lst=hf_outputs,
+            outputs_1_lst=vllm_outputs,
+            name_0="hf",
+            name_1="vllm",
+        )
 
 
 @pytest.mark.parametrize("model", MODELS)

vllm/platforms/cuda.py

@@ -7,6 +7,7 @@
 from typing import Callable, List, Tuple, TypeVar
 
 import pynvml
+import torch
 from typing_extensions import ParamSpec
 
 from vllm.logger import init_logger
@@ -26,6 +27,10 @@
         " and cause errors. See https://pypi.org/project/pynvml "
         "for more information.")
 
+# pytorch 2.5 uses cudnn sdpa by default, which will cause crash on some models
+# see https://github.com/huggingface/diffusers/issues/9704 for details
+torch.backends.cuda.enable_cudnn_sdp(False)
+
 # NVML utils
 # Note that NVML is not affected by `CUDA_VISIBLE_DEVICES`,
 # all the related functions work on real physical device ids.